Effects of the self-perceived sensorimotor demand and immersion during video gaming on visual attention skills

Playing specific genres of video games (e.g., action video games) has been linked to improvements in cognitive skills mostly related to attentional phenomena. Nonetheless, do video games have features or dimensions in common that impact cognitive improvements beyond the game genre? Here we argue that the sensorimotor demand-the amount of demand for precise coordination between movement and perception-is a key element in the improvements associated with playing video games. We conducted a two-part study to test this hypothesis: a self-report online gaming instrument development and validation and an in-lab behavioral and electrophysiological study. In the first study, data from 209 participants were used to devise the Sensory-Motor Demand Instrument (SMDI). The SMDI was split into three dimensions of video game playing: sensorimotor contingency, immersion, and unfocused gaming. Criterion validity related to video gamers' characteristics supported that the SMDI is sensitive to the input device (e.g., keyboard or touchscreens) and the most recent experience gained during gaming sessions while not being sensitive to the game genre. In the second study, data from 20 participants who performed four visual-attentional tasks previously reported in the literature showed that the SMDI's dimensions were associated with behavioral performance measures and the latency and amplitude of Event-Related Potentials (N1, P2, and P3). Despite the challenge of studying the video gamer population, our study remarks on the relevance of sensorimotor demands in the performance of attentional tasks and its potential use as a dimension to characterize the experience of playing video games beyond the game genre.

Eating contexts determine the efficacy of nutrient warning labels to promote healthy food choices

Introduction

Unhealthy food choices increase the risk of obesity and its co-morbidities. Nutrition labels are a public health policy that aims to drive individuals toward healthier food choices. Chile has been an example of this policy, where mandatory nutrient warning labels (NWL) identify processed foods high in calories and critical nutrients. Eating contexts influence individual food choices, but whether eating contexts also influence how NWL alter the decision process and selection during food choice is unknown.

Methods

In an online mouse-tracking study, participants prompted to health, typical, or unrestricted eating contexts were instructed to choose between pairs of foods in the presence or absence of NWL. Conflict during choices was analyzed using mouse paths and reaction times.

Results

NWL increased conflict during unhealthy food choices and reduced conflict during healthy choices in all contexts. However, the probability that NWL reversed an unhealthy choice was 80% in a healthy, 37% in a typical, and 19% in an unrestricted context. A drift-diffusion model analysis showed the effects of NWL on choice were associated with an increased bias toward healthier foods in the healthy and typical but not in the unrestricted context.

Discussion

These data suggest that the efficacy of NWL to drive healthy food choices increases in a healthy eating context, whereas NWL are less effective in typical or unrestricted eating contexts.

Voluntary self-initiation of the stimuli onset improves working memory and accelerates visual and attentional processing

The ability of an organism to voluntarily control the stimuli onset modulates perceptual and attentional functions. Since stimulus encoding is an essential component of working memory (WM), we conjectured that controlling the initiation of the perceptual process would positively modulate WM. To corroborate this proposition, we tested twenty-five healthy subjects in a modified-Sternberg WM task under three stimuli presentation conditions: an automatic presentation of the stimuli, a self-initiated presentation of the stimuli (through a button press), and a self-initiated presentation with random-delay stimuli onset. Concurrently, we recorded the subjects' electroencephalographic signals during WM encoding. We found that the self-initiated condition was associated with better WM accuracy, and earlier latencies of N1, P2 and P3 evoked potential components representing visual, attentional and mental review of the stimuli processes, respectively. Our work demonstrates that self-initiated stimuli enhance WM performance and accelerate early visual and attentional processes deployed during WM encoding. We also found that self-initiated stimuli correlate with an increased attentional state compared to the other two conditions, suggesting a role for temporal stimuli predictability. Our study remarks on the relevance of self-control of the stimuli onset in sensory, attentional and memory updating processing for WM.

Infants exploit vowels to label objects and actions from continuous audiovisual stimuli

Before the 6-months of age, infants succeed to learn words associated with objects and actions when the words are presented isolated or embedded in short utterances. It remains unclear whether such type of learning occurs from fluent audiovisual stimuli, although in natural environments the fluent audiovisual contexts are the default. In 4 experiments, we evaluated if 8-month-old infants could learn word-action and word-object associations from fluent audiovisual streams when the words conveyed either vowel or consonant harmony, two phonological cues that benefit word learning near 6 and 12 months of age, respectively. We found that infants learned both types of words, but only when the words contained vowel harmony. Because object- and action-words have been conceived as rudimentary representations of nouns and verbs, our results suggest that vowels contribute to shape the initial steps of the learning of lexical categories in preverbal infants.

Biomechanical analysis of expert anesthesiologists and novice residents performing a simulated central venous access procedure

BACKGROUND

Central venous access (CVA) is a frequent procedure taught in medical residencies. However, since CVA is a high-risk procedure requiring a detailed teaching and learning process to ensure trainee proficiency, it is necessary to determine objective differences between the expert's and the novice's performance to guide novice practitioners during their training process. This study compares experts' and novices' biomechanical variables during a simulated CVA performance.

METHODS

Seven experts and seven novices were part of this study. The participants' motion data during a CVA simulation procedure was collected using the Vicon Motion System. The procedure was divided into four stages for analysis, and each hand's speed, acceleration, and jerk were obtained. Also, the procedural time was analyzed. Descriptive analysis and multilevel linear models with random intercept and interaction were used to analyze group, hand, and stage differences.

RESULTS

There were statistically significant differences between experts and novices regarding time, speed, acceleration, and jerk during a simulated CVA performance. These differences vary significantly by the procedure stage for right-hand acceleration and left-hand jerk.

CONCLUSIONS

Experts take less time to perform the CVA procedure, which is reflected in higher speed, acceleration, and jerk values. This difference varies according to the procedure's stage, depending on the hand and variable studied, demonstrating that these variables could play an essential role in differentiating between experts and novices, and could be used when designing training strategies.

Cortico-Striatal Oscillations Are Correlated to Motor Activity Levels in Both Physiological and Parkinsonian Conditions

Oscillatory neural activity in the cortico-basal ganglia-thalamocortical (CBGTC) loop is associated with the motor state of a subject, but also with the availability of modulatory neurotransmitters. For example, increased low-frequency oscillations in Parkinson’s disease (PD) are related to decreased levels of dopamine and have been proposed as biomarkers to adapt and optimize therapeutic interventions, such as deep brain stimulation. Using neural oscillations as biomarkers require differentiating between changes in oscillatory patterns associated with parkinsonism vs. those related to a subject’s motor state. To address this point, we studied the correlation between neural oscillatory activity in the motor cortex and striatum and varying degrees of motor activity under normal and parkinsonian conditions. Using rats with bilateral or unilateral 6-hydroxydopamine lesions as PD models, we correlated the motion index (MI)—a measure based on the physical acceleration of the head of rats—to the local field potential (LFP) oscillatory power in the 1–80 Hz range. In motor cortices and striata, we observed a robust correlation between the motion index and the oscillatory power in two main broad frequency ranges: a low-frequency range [5.0–26.5 Hz] was negatively correlated to motor activity, whereas a high-frequency range [35.0–79.9 Hz] was positively correlated. We observed these correlations in both normal and parkinsonian conditions. In addition to these general changes in broad-band power, we observed a more restricted narrow-band oscillation [25–40 Hz] in dopamine-denervated hemispheres. This oscillation, which seems to be selective to the parkinsonian state, showed a linear frequency dependence on the concurrent motor activity level. We conclude that, independently of the parkinsonian condition, changes in broad-band oscillatory activities of cortico-basal ganglia networks (including changes in the relative power of low- and high-frequency bands) are closely correlated to ongoing motions, most likely reflecting he operations of these neural circuits to control motor activity. Hence, biomarkers based on neural oscillations should focus on specific features, such as narrow frequency bands, to allow differentiation between parkinsonian states and physiological movement-dependent circuit modulation.

The Energy Homeostasis Principle: Neuronal Energy Regulation Drives Local Network Dynamics Generating Behavior

A major goal of neuroscience is understanding how neurons arrange themselves into neural networks that result in behavior. Most theoretical and experimental efforts have focused on a top-down approach which seeks to identify neuronal correlates of behaviors. This has been accomplished by effectively mapping specific behaviors to distinct neural patterns, or by creating computational models that produce a desired behavioral outcome. Nonetheless, these approaches have only implicitly considered the fact that neural tissue, like any other physical system, is subjected to several restrictions and boundaries of operations. Here, we proposed a new, bottom-up conceptual paradigm: The Energy Homeostasis Principle, where the balance between energy income, expenditure, and availability are the key parameters in determining the dynamics of neuronal phenomena found from molecular to behavioral levels. Neurons display high energy consumption relative to other cells, with metabolic consumption of the brain representing 20% of the whole-body oxygen uptake, contrasting with this organ representing only 2% of the body weight. Also, neurons have specialized surrounding tissue providing the necessary energy which, in the case of the brain, is provided by astrocytes. Moreover, and unlike other cell types with high energy demands such as muscle cells, neurons have strict aerobic metabolism. These facts indicate that neurons are highly sensitive to energy limitations, with Gibb's free energy dictating the direction of all cellular metabolic processes. From this activity, the largest energy, by far, is expended by action potentials and post-synaptic potentials; therefore, plasticity can be reinterpreted in terms of their energy context. Consequently, neurons, through their synapses, impose energy demands over post-synaptic neurons in a close loop-manner, modulating the dynamics of local circuits. Subsequently, the energy dynamics end up impacting the homeostatic mechanisms of neuronal networks. Furthermore, local energy management also emerges as a neural population property, where most of the energy expenses are triggered by sensory or other modulatory inputs. Local energy management in neurons may be sufficient to explain the emergence of behavior, enabling the assessment of which properties arise in neural circuits and how. Essentially, the proposal of the Energy Homeostasis Principle is also readily testable for simple neuronal networks.

Finger Temperature: A Psychophysiological Assessment of the Attentional State

Attention is a key cognitive phenomenon that is studied to understand cognitive disorders or even to estimate workloads to prevent accidents. Usually, it is studied using brain activity, even though it has many psychophysiological correlates. In the present study, we aim to evaluate if finger temperature, as a surrogate of peripheral vasoconstriction, can be used to obtain similar and complementary information to electroencephalography (EEG) brain activity measurements. To conduct this, 34 participants were recruited and submitted to performing four tasks-one as a baseline, and three attentional tasks. These three attentional tasks measured sustained attention, resilience to distractors, and attentional resources. During the tasks, the room, forehead, tympanic, and finger temperatures were measured. Furthermore, we included a 32-channel EEG recording. Our results showed a strong monotonic association between the finger temperature and the Alpha and Beta EEG spectral bands. When predicting attentional performance, the finger temperature was complementary to the EEG spectral measurements, through the prediction of aspects of attentional performance that had not been assessed by spectral EEG activity, or through the improvement of the model's fit. We also found that during the baseline task (non-goal-oriented task), the spectral EEG activity has an inverted correlation, as compared to a goal-oriented task. Our current results suggest that the psychophysiological assessment of attention is complementary to classic EEG approach, while also having the advantage of easy implementation of analysis tools in environments of reducing control (workplaces, student classrooms).

Cold-Blooded Attention: Finger Temperature Predicts Attentional Performance

Thermal stress has been shown to increase the chances of unsafe behavior during industrial and driving performances due to reductions in mental and attentional resources. Nonetheless, establishing appropriate safety standards regarding environmental temperature has been a major problem, as modulations are also be affected by the task type, complexity, workload, duration, and previous experience with the task. To bypass this attentional and thermoregulatory problem, we focused on the body rather than environmental temperature. Specifically, we measured tympanic, forehead, finger and environmental temperatures accompanied by a battery of attentional tasks. We considered a 10 min baseline period wherein subjects were instructed to sit and relax, followed by three attentional tasks: a continuous performance task (CPT), a flanker task (FT) and a counting task (CT). Using multiple linear regression models, we evaluated which variable(s) were the best predictors of performance. The results showed a decrement in finger temperature due to instruction and task engagement that was absent when the subject was instructed to relax. No changes were observed in tympanic or forehead temperatures, while the environmental temperature remained almost constant for each subject. Specifically, the magnitude of the change in finger temperature was the best predictor of performance in all three attentional tasks. The results presented here suggest that finger temperature can be used as a predictor of alertness, as it predicted performance in attentional tasks better than environmental temperature. These findings strongly support that peripheral temperature can be used as a tool to prevent unsafe behaviors and accidents.

Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task

Our daily interaction with the world is plagued of situations in which we develop expertise through self-motivated repetition of the same task. In many of these interactions, and especially when dealing with computer and machine interfaces, we must deal with sequences of decisions and actions. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion and a specific sequence of choices must be performed in order to produce the expected outcome. But, as we become experts in the use of such interfaces, is it possible to identify specific search and learning strategies? And if so, can we use this information to predict future actions? In addition to better understanding the cognitive processes underlying sequential decision making, this could allow building adaptive interfaces that can facilitate interaction at different moments of the learning curve. Here we tackle the question of modeling sequential decision-making behavior in a simple human-computer interface that instantiates a 4-level binary decision tree (BDT) task. We record behavioral data from voluntary participants while they attempt to solve the task. Using a Hidden Markov Model-based approach that capitalizes on the hierarchical structure of behavior, we then model their performance during the interaction. Our results show that partitioning the problem space into a small set of hierarchically related stereotyped strategies can potentially capture a host of individual decision making policies. This allows us to follow how participants learn and develop expertise in the use of the interface. Moreover, using a Mixture of Experts based on these stereotyped strategies, the model is able to predict the behavior of participants that master the task.